# 预测保护（Prediction Guard）

本页面介绍如何在 LangChain 中使用预测保护生态系统。
它分为两部分：安装和设置，以及对特定预测保护封装的引用。

## 安装和设置
- 使用 `pip install predictionguard` 安装 Python SDK
- 获取一个预测保护访问令牌（如 [此处](https://docs.predictionguard.com/) 所述），并将其设置为环境变量（`PREDICTIONGUARD_TOKEN`）

## LLM 封装器

存在一个预测保护 LLM 封装器，您可以通过以下方式访问：
```python
from langchain.llms import PredictionGuard
```

在初始化 LLM 时，可以通过参数提供预测保护模型的名称：
```python
pgllm = PredictionGuard(model="MPT-7B-Instruct")
```

您还可以直接通过参数提供访问令牌：
```python
pgllm = PredictionGuard(model="MPT-7B-Instruct", token="<your access token>")
```

最后，您可以提供一个“output”参数，用于结构化/控制 LLM 的输出：
```python
pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"})
```

## 示例用法

使用受控或受保护的 LLM 封装器的基本用法：
```python
import os

import predictionguard as pg
from langchain.llms import PredictionGuard
from langchain import PromptTemplate, LLMChain

# Your Prediction Guard API key. Get one at predictionguard.com
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"

# Define a prompt template
template = """Respond to the following query based on the context.

Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦
Exclusive Candle Box - $80 
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Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉

Query: {query}

Result: """
prompt = PromptTemplate(template=template, input_variables=["query"])

# With "guarding" or controlling the output of the LLM. See the 
# Prediction Guard docs (https://docs.predictionguard.com) to learn how to 
# control the output with integer, float, boolean, JSON, and other types and
# structures.
pgllm = PredictionGuard(model="MPT-7B-Instruct", 
                        output={
                                "type": "categorical",
                                "categories": [
                                    "product announcement", 
                                    "apology", 
                                    "relational"
                                    ]
                                })
pgllm(prompt.format(query="What kind of post is this?"))
```

使用预测保护封装器进行基本的 LLM 链式调用：
```python
import os

from langchain import PromptTemplate, LLMChain
from langchain.llms import PredictionGuard

# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows
# you to access all the latest open access models (see https://docs.predictionguard.com)
os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"

# Your Prediction Guard API key. Get one at predictionguard.com
os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"

pgllm = PredictionGuard(model="OpenAI-text-davinci-003")

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.predict(question=question)
```